Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis
Bibliographic record
Abstract
Developers often use microbenchmarking tools to evaluate the performance of a Java program. These tools run a small section of code multiple times and measure its performance. However, this process can be problematic as Java execution is traditionally divided into two stages: a warmup stage where the JVM's JIT compiler optimizes frequently used code and a steady stage where performance is stable. Measuring performance before reaching the steady stage can provide an inaccurate representation of the program's efficiency. The challenge comes from determining when a program should be considered as in a steady state. In this paper, we propose that call stack sampling data should be considered when conducting steady state performance evaluations. By analyzing this data, we can generate call graphs for individual microbenchmark executions. Our proposed method of using call stack sampling data and visualizing call graphs intuitively empowers developers to effectively distinguish between warmup and steady state executions. Additionally, by utilizing machine learning classification techniques this method can automate the steady state detection, working towards a more accurate and efficient performance evaluation process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".